Using DDPG for control in multi-dimensional continuous action space?
First, is it even possible to use DDPG for multi-dimensional
continuous action spaces?

Yes, DDPG was primarily developed to deal with continuous action space you can find out more here, here and here.

I have not found any code examples to learn from and many of the
papers I have read are near the limit of my understanding in this
area.

You can check kerasRL ddpg implementation it is quite easy to understand and can solve your problem of a model with multiple outputs..

why might my actor network be outputting values clustered near its
max/min values, and why would the values in either cluster all be the
same?

I think it is just a convergence problem try out small learning rates and/or small target_model_update param.
If this doesn't work for you check TRPO and/or PPO..